scholarly journals Validation of the World Health Organization/ International Society of Hypertension (WHO/ISH) cardiovascular risk predictions in Sri Lankans based on findings from a prospective cohort study

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252267
Author(s):  
U. B. Thulani ◽  
K. C. D. Mettananda ◽  
D. T. D. Warnakulasuriya ◽  
T. S. G. Peiris ◽  
K. T. A. A. Kasturiratne ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Medical Officer ◽  
Population Based ◽  
World Health ◽  
Risk Category ◽  
Risk Prediction Models ◽  
Risk Charts ◽  
Health Organization

Introduction and objectives There are no cardiovascular (CV) risk prediction models for Sri Lankans. Different risk prediction models not validated for Sri Lankans are being used to predict CV risk of Sri Lankans. We validated the WHO/ISH (SEAR-B) risk prediction charts prospectively in a population-based cohort of Sri Lankans. Method We selected 40–64 year-old participants from the Ragama Medical Officer of Health (MOH) area in 2007 by stratified random sampling and followed them up for 10 years. Ten-year risk predictions of a fatal/non-fatal cardiovascular event (CVE) in 2007 were calculated using WHO/ISH (SEAR-B) charts with and without cholesterol. The CVEs that occurred from 2007–2017 were ascertained. Risk predictions in 2007 were validated against observed CVEs in 2017. Results Of 2517 participants, the mean age was 53.7 year (SD: 6.7) and 1132 (45%) were males. Using WHO/ISH chart with cholesterol, the percentages of subjects with a 10-year CV risk <10%, 10–19%, 20%-29%, 30–39%, ≥40% were 80.7%, 9.9%, 3.8%, 2.5% and 3.1%, respectively. 142 non-fatal and 73 fatal CVEs were observed during follow-up. Among the cohort, 9.4% were predicted of having a CV risk ≥20% and 8.6% CVEs were observed in the risk category. CVEs were within the predictions of WHO/ISH charts with and without cholesterol in both high (≥20%) and low(<20%) risk males, but only in low(<20%) risk females. The predictions of WHO/ISH charts, with-and without-cholesterol were in agreement in 81% of subjects (ĸ = 0.429; p<0.001). Conclusions WHO/ISH (SEAR B) risk prediction charts with-and without-cholesterol may be used in Sri Lanka. Risk charts are more predictive in males than in females and for lower-risk categories. The predictions when stratifying into 2 categories, low risk (<20%) and high risk (≥20%), are more appropriate in clinical practice.

scholarly journals New World Health Organization reference values for semen analysis: where do we stand?

2013 ◽  
Vol 11 (2) ◽  
pp. 263-264 ◽  
Author(s):  
Marcelo Vieira
Keyword(s):  
World Health Organization ◽  
Reference Values ◽  
New World ◽  
Semen Analysis ◽  
Population Based ◽  
World Health ◽  
The World ◽  
Health Organization ◽  
References Values

Semen analysis is of paramount importance to study potential male fertility, couple's infertility, the effects of gonadotoxic agents on spermatogenesis and as follow-up test during treatment of male infertility. Since 1987, the World Health Organization proposes the standardization of this test and its reference values based on population-based data. The latest version of the World Health Organization guidelines was published in 2010. It introduced a new methodology that produced new references values, which triggered a discussion that lies inconclusive. We revised the original World Health Organization paper focusing on methodological changes and its results, the new references values and their impact on clinical practice.

scholarly journals P0343RISK PREDICTION MODELS FOR PREDICTING PROGRESSION OF IGA NEPHROLOGY:A VALIDATION STUDY

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Jingyuan Xie
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Collaborative Study ◽  
Clinical Parameters ◽  
Full Model ◽  
Risk Prediction Models ◽  
Clinical Model ◽  
C Statistic ◽  
Model C

Abstract Background and Aims Risk prediction models for IgA nephropathy (IgAN) containing clinical variables (clinical model) or clinical plus pathological variables (full model) have been established based on a large international collaborative study recently, but external validation of these models are still required before clinical application. The aim of this study is to externally validate previously reported risk prediction models based on our multi-center IgAN cohort. Method Biopsy-proven IgAN patients with eGFR ≥15 ml/min/1.73 m2 at baseline and a minimum follow-up of 6 months were enrolled. Primary outcome was defined as end-stage kidney disease (ESRD). Cox proportional hazards models were built to validate risk models. R2, Akaike information criterion (AIC) and C statistic were calculated to evaluate model accuracy. Results A total of 2300 IgAN patients with a median follow-up of 30 months were enrolled, and 214(9.3%) ESRD occurred during the follow-up period. The median age was 35(interquartile range, 28-44) years, and 1106 cases (48.1%) were men. Our cohort successfully validated the clinical model and the full model based on C statistic (0.90 and 0.91) and R2 (0.32 and 0.32). Our results showed limited improvement in model performance after adding the Oxford classification parameters to clinical parameters. However, both two models performed better than the model consisting only pathological parameters(C statistic 0.83, R2 0.24). We also validated other risk prediction models, including CLIN model (C statistic 0.90, R2 0.32) and CLINPATH model (C statistic 0.91, R2 0.31) derived from Chinese IgAN patients or CKD model (C statistic 0.90, R2 0.32) derived from Canadian CKD patients. It was found that clinical models based on different combinations of clinical parameters performed similarly. Conclusion In summary, we successfully validated a recently reported IgAN risk model and we found that clinical parameters alone could accurately predict ESRD risk in IgAN patients.

scholarly journals An external validation of the QCovid risk prediction algorithm for risk of mortality from COVID-19 in adults: national validation cohort study in England

2021 ◽  
Author(s):  
Vahe Nafilyan ◽  
Ben Humberstone ◽  
Nisha Mehta ◽  
Ian Diamond ◽  
Carol Coupland ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Prediction Models ◽  
Validation Cohort ◽  
Population Based ◽  
Prediction Algorithm ◽  
Risk Prediction Models ◽  
Men And Women ◽  
Time Period ◽  
Time Periods ◽  
First Time

SUMMARYBackgroundTo externally validate a risk prediction algorithm (QCovid) to estimate mortality outcomes from COVID-19 in adults in England.MethodsPopulation-based cohort study using the ONS Public Health Linked Data Asset, a cohort based on the 2011 Census linked to Hospital Episode Statistics, the General Practice Extraction Service Data for pandemic planning and research, radiotherapy and systemic chemotherapy records. The primary outcome was time to COVID-19 death, defined as confirmed or suspected COVID-19 death as per death certification. Two time periods were used: (a) 24th January to 30th April 2020; and (b) 1st May to 28th July 2020. We evaluated the performance of the QCovid algorithms using measures of discrimination and calibration for each validation time period.FindingsThe study comprises 34,897,648 adults aged 19-100 years resident in England. There were 26,985 COVID-19 deaths during the first time-period and 13,177 during the second. The algorithms had good calibration in the validation cohort in both time periods with close correspondence of observed and predicted risks. They explained 77.1% (95% CI: 76.9% to 77.4%) of the variation in time to death in men in the first time-period (R2); the D statistic was 3.76 (95% CI: 3.73 to 3.79); Harrell’s C was 0.935 (0.933 to 0.937). Similar results were obtained for women, and in the second time-period. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths in the first time period was 65.9% for men and 71.7% for women. People in the top 20% of predicted risks of death accounted for 90.8% of all COVID-19 deaths for men and 93.0% for women.InterpretationThe QCovid population-based risk algorithm performed well, showing very high levels of discrimination for COVID-19 deaths in men and women for both time periods. It has the potential to be dynamically updated as the pandemic evolves and therefore, has potential use in guiding national policy.FundingNational Institute of Health ResearchRESEARCH IN CONTEXTEvidence before this studyPublic policy measures and clinical risk assessment relevant to COVID-19 need to be aided by rigorously developed and validated risk prediction models. A recent living systematic review of published risk prediction models for COVID-19 found most models are subject to a high risk of bias with optimistic reported performance, raising concern that these models may be unreliable when applied in practice. A population-based risk prediction model, QCovid risk prediction algorithm, has recently been developed to identify adults at high risk of serious COVID-19 outcomes, which overcome many of the limitations of previous tools.Added value of this studyCommissioned by the Chief Medical Officer for England, we validated the novel clinical risk prediction model (QCovid) to identify risks of short-term severe outcomes due to COVID-19. We used national linked datasets from general practice, death registry and hospital episode data for a population-representative sample of over 34 million adults. The risk models have excellent discrimination in men and women (Harrell’s C statistic>0.9) and are well calibrated. QCovid represents a new, evidence-based opportunity for population risk-stratification.Implications of all the available evidenceQCovid has the potential to support public health policy, from enabling shared decision making between clinicians and patients in relation to health and work risks, to targeted recruitment for clinical trials, and prioritisation of vaccination, for example.

Differences in Clinical and Molecular Characteristics and Outcome in Prefibrotic and Overt Primary Myelofibrosis According to 2016 WHO Criteria. a Study on 639 Patients of the Agimm Group

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 943-943
Author(s):  
Paola Guglielmelli ◽  
Giada Rotunno ◽  
Annalisa Pacilli ◽  
Elisa Rumi ◽  
Vittorio Rosti ◽  
...  
Keyword(s):  
Reference Group ◽  
Primary Myelofibrosis ◽  
World Health ◽  
Driver Mutations ◽  
Molecular Characteristics ◽  
Risk Category ◽  
Type I ◽  
Who Criteria ◽  
Health Organization

Abstract The revised 2016 World Health Organization (WHO) classification of myeloid neoplasms dictated distinct criteria for prefibrotic (prePMF) and overt primary myelofibrosis (PMF), based on bone marrow (BM) morphology, including fibrosis grade (G)<1 in prePMF and G2-3 in PMF, and presence of leukoerythroblastosis in PMF. AIM: to describe the characteristics and outcome of patients (pts) with a diagnosis of prePMF versus PMF according to the 2016 WHO criteria. METHODS. We used a database of ca 800 pts collected in 5 Italian tertiary centers of the AGIMM project. Pts annotated with diagnosis of pre/early PMF and PMF were identified, and BM biopsies were re-classified according to current criteria. A total of 639 pts with full information were retrieved; all were annotated for both driver mutations (JAK2V617F, MPLW515x, CALR) and High Molecular Risk mutations (HMR; Vannucchi et al, Leukemia 2013;1861), including ASXL1, EZH2, SRSF2, IDH1/2. RESULTS. Of the 639 pts, 274 (42.8%) were re-classified as prePMF and 365 (57.2%) as PMF. After a median follow-up (FU) of 3.6y, 212 pts (33.2%) had died, 23% prePMF vs 40.8% PMF (P<.0001); 69 pts (10.8%) transformed to leukemia (AL), 8.0% vs 12.9% in prePMF vs PMF (P= .033). At diagnosis, compared to PMF, prePMF pts were enriched in females (57.2% vs 42.9%, P=.01), were younger (59.4 vs 64.7y; P<.001) and with less >65y old individuals (36.1% vs 46.8%, P= .004), had higher Hb (12.7 vs 10.7g/dL; P<.0001) and fewer anemic subjects (Hb<10g/dL:14.2% vs 38.4%; P<.0001), showed higher platelet (plt) count (453 vs 247x109/L; P<.0001) and fewer thrombocytopenic subjects (<100x109/L; 8.0% vs 18.1%; P<.0001); blasts >1% were found in 12.0% vs 26.3% (P<.0001), while leukocyte count and % of pts with >25x109/L were similar. Abnormal karyotype in 18.5% prePMF vs 38.6% PMF (n=317; P<.0001). Constitutional symptoms were reported in 20.8% prePMF vs 34.3% PMF (P<.0001), palpable splenomegaly in 64.2% vs 83.1% (P<.0001), spleen >10cm in 10.5% vs 24.1% (P<.0001). Major thrombosis occurred in 15.5% of prePMF vs 9.0% PMF (P=.02). According to IPSS, 74.2% and 25.8% of prePMF pts were in the lower and higher risk categories, respectively, vs 50.8% and 49.2% of PMF (P<.0001). At the latest FU, most prePMF pts maintained a lower risk category (65.8%) according to DIPSS unlike PMF with 69.3% being categorized as higher risk (P<.0001). The proportion of patients with JAK2V617F (and their median allele burden), MPLW515x and CALR (type I and type II) mutations was similar in prePMF and PMF; triple-negative (TN) pts were slightly more frequent in PMF (14%) than prePMF (9.9%; P=.041). Conversely, 24.8% of prePMF pts vs 41.1% PMF were HMR (P<.0001), the frequent mutated genes were ASXL1 and EZH2; >2 HMR mutations, that are prognostically negative (Guglielmelli et al, Leukemia 2014; 28), were found in 11.8% of PMF pts vs 4.7% prePMF (P<.0001). Median survival (OS) was 17.6y in prePMF vs 7.2y in PMF (P<.0001). OS was accurately predicted by IPSS in both prePMF and PMF. Using CALR type 1 mutation as the reference group, CALR type 2, JAK2/MPL mutations and triple negativity were negative predictors. HMR status was prognostically significant for OS in both prePMF (HR1.8, 95%CI 1.1-3.0, P=.03) and PMF (HR 2.5; 1.8-3.4, P<.0001) as it was >2 HMR mutations (HR 9.3, 4.5-19.0 and 3.4,2.1-5.3, respectively; P<.0001). CONCLUSIONS. This analysis of pts with contemporary diagnosis of prePMF and PMF disclosed important clinical, hematologic and molecular differences between the two, and indirectly suggested that they might represent a phenotypic continuum where increased grade of fibrosis associates with worsening of disease manifestations and outcome. Disclosures Vannucchi: Novartis: Consultancy, Research Funding, Speakers Bureau; Baxalta: Speakers Bureau; Shire: Speakers Bureau.

Risk prediction model for heart disease among endometrial cancer survivors.

2018 ◽  
Vol 36 (7_suppl) ◽  
pp. 120-120
Author(s):  
Mia Hashibe ◽  
Brenna Blackburn ◽  
Jihye Park ◽  
Kerry G. Rowe ◽  
John Snyder ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Prediction Model ◽  
Cancer Survivors ◽  
Risk Prediction ◽  
Prediction Models ◽  
Population Based ◽  
Risk Prediction Model ◽  
High Cholesterol ◽  
Risk Prediction Models ◽  
History Of

120 Background: There are an estimated 760,000 endometrial cancer survivors alive in the US today. We previously reported on increased heart disease (HD) risk among endometrial cancer survivors from our population-based cohort study. Although there are many risk prediction models for the risk of endometrial cancer, there are none to our knowledge for endometrial cancer survivors. Methods: We identified 2,994 endometrial cancer patients in the Utah Population Database, which links data from multiple statewide sources. We estimated hazard ratios with the Cox proportional hazards model for predictors of five-, ten- and fifteen-year risks. The Harrell’s C statistic was used to evaluate the model performance. We used 70% of the data randomly selected to develop the model and the rest of the data to validate the model. Results: A total of 1,591 patients were diagnosed with HD. Increased risks of HD among endometrial cancer patients were observed for older age, obesity at baseline, family history of HD, previous disease diagnosis (hypertension, diabetes, high cholesterol, COPD), distant stage, grade, histology, chemotherapy, and radiation therapy. The C-statistics for the risk prediction model were 0.69 for the hypothesized risk factors for HD, 0.56 for clinical factors, and 0.71 when statistically significant risk factors were included. With the final model selected, as one example, the absolute risks of HD were 17.6% at 5-years, 24.0% at 10-years and 32.0% at 15 years for a woman diagnosed with regional stage, grade I endometrial cancer in her fifties, was white, was obese at cancer diagnosis, had a family history of HD but no previous history of HD herself, had hypertension, but no history of diabetes or high cholesterol or COPD, and had radiation therapy treatment but no chemotherapy. The AUCs were 0.79 for the 5-year, 0.78 for the 10-year and 0.78 for the 15-year predictions. Conclusions: We developed the first risk prediction model for HD among endometrial cancer survivors within a population-based cohort study. Risk prediction models for cancer survivors are important in understanding long-term disease risks after cancer treatment is complete. Such models may contribute to management plans for treatment and individualized prevention efforts.

scholarly journals Validation of risk prediction models for the development of HBV-related HCC: a retrospective multi-center 10-year follow-up cohort study

Oncotarget ◽  
2017 ◽  
Vol 8 (68) ◽  
pp. 113213-113224 ◽  
Author(s):  
Yeon Seok Seo ◽  
Byoung Kuk Jang ◽  
Soon Ho Um ◽  
Jae Seok Hwang ◽  
Kwang-Hyub Han ◽  
...  
Keyword(s):  
Cohort Study ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models

scholarly journals Long-term maintenance rituximab for ANCA-associated vasculitis: relapse and infection prediction models

Rheumatology ◽  
2020 ◽  
Author(s):  
Mark E McClure ◽  
Yajing Zhu ◽  
Rona M Smith ◽  
Seerapani Gopaluni ◽  
Joanna Tieu ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Groups ◽  
Risk Of Infection ◽  
Risk Prediction Models ◽  
Anca Associated Vasculitis ◽  
Term Maintenance

Abstract Objectives Following a maintenance course of rituximab (RTX) for ANCA-associated vasculitis (AAV), relapses occur on cessation of therapy, and further dosing is considered. This study aimed to develop relapse and infection risk prediction models to help guide decision making regarding extended RTX maintenance therapy. Methods Patients with a diagnosis of AAV who received 4–8 grams of RTX as maintenance treatment between 2002 and 2018 were included. Both induction and maintenance doses were included; most patients received standard departmental protocol consisting of 2× 1000 mg 2 weeks apart, followed by 1000 mg every 6 months for 2 years. Patients who continued on repeat RTX dosing long-term were excluded. Separate risk prediction models were derived for the outcomes of relapse and infection. Results A total of 147 patients were included in this study with a median follow-up of 63 months [interquartile range (IQR): 34–93]. Relapse: At time of last RTX, the model comprised seven predictors, with a corresponding C-index of 0.54. Discrimination between individuals using this model was not possible; however, discrimination could be achieved by grouping patients into low- and high-risk groups. When the model was applied 12 months post last RTX, the ability to discriminate relapse risk between individuals improved (C-index 0.65), and once again, clear discrimination was observed between patients from low- and high-risk groups. Infection: At time of last RTX, five predictors were retained in the model. The C-index was 0.64 allowing discrimination between low and high risk of infection groups. At 12 months post RTX, the C-index for the model was 0.63. Again, clear separation of patients from two risk groups was observed. Conclusion While our models had insufficient power to discriminate risk between individual patients they were able to assign patients into risk groups for both relapse and infection. The ability to identify risk groups may help in decisions regarding the potential benefit of ongoing RTX treatment. However, we caution the use of these prediction models until prospective multi-centre validation studies have been performed.

scholarly journals Prediction of Cardiovascular Disease Risk Accounting for Future Initiation of Statin Treatment

2021 ◽  
Author(s):  
Zhe Xu ◽  
Matthew Arnold ◽  
David Stevens ◽  
Stephen Kaptoge ◽  
Lisa Pennells ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
High Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Predictive Performance ◽  
Statin Treatment ◽  
Clinical Practice Research Datalink ◽  
Cvd Risk ◽  
Risk Prediction Models

Abstract Cardiovascular disease (CVD) risk prediction models are used to identify high-risk individuals and guide statin-initiation. However, these models are usually derived from individuals who may initiate statins during follow-up. We present a simple approach to address statin-initiation to predict “statin-naïve” CVD risk. We analyzed primary care data (2004-2017) from the UK Clinical Practice Research Datalink for 1,678,727 individuals (40-85 years) without CVD or statin treatment history at study entry. We derived age- and sex-specific prediction models including conventional risk factors and a time-dependent effect of statin-initiation constrained to 25% risk reduction (from trial results). We compared predictive performance and measures of public-health impact (e.g., numbers-needed-to-screen to prevent one case) against models ignoring statin-initiation. During a median follow-up of 8.9 years, 103,163 individuals developed CVD. In models accounting for versus ignoring statin initiation, 10-year CVD risk predictions were slightly higher; predictive performance was moderately improved. However, few individuals were reclassified to a high-risk threshold, resulting in negligible improvements in numbers-needed-to-screen to prevent one case. In conclusion, incorporating statin effects from trial results into risk prediction models enables statin-naïve CVD risk estimation, provides moderate gains in predictive ability, but had a limited impact on treatment decision-making under current guidelines in this population.

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